

When the dynamics is unknown, learning an accurate \(f\)  becomes essential for e.g. planning and control [1]}, [2]}, [3]}. Usually, this map is approximated by a parametric function, \(f_{\theta }\) , through minimization of some loss between the predicted next state \(\Delta \hat{s}_{i+1} = f_{\theta }(s_i, a_i)\)  and the true next state \(s_i\) :
\(\theta ^{*} = \underset{\theta }{\arg \min } \frac{1}{N}\sum _{i=0}^N loss(\hat{s}_{i+1}, s_{i+1}).\) 
